Baseten

Baseten is a managed infrastructure and tooling platform for deploying, scaling, and operating Machine Learning (ML) models in production environments.

• Managed infrastructure for serving and scaling ML models (AI infrastructure)
• Model deployment workflows with versioning, configuration, and rollout controls (MLOps)
• Inference optimization features such as autoscaling, hardware selection, and performance tuning (AI infrastructure)
• APIs and developer tooling for integrating model inference into applications and services (Developer platforms)
• Support for modern ML frameworks and cloud-native architectures for production workloads (MLOps)

More About Baseten

Baseten provides a platform for organizations that need to run ML models as production services, with a focus on infrastructure, orchestration, and developer workflows rather than model training. Enterprise teams can use Baseten to deploy trained models from common ML frameworks and expose them as scalable, monitored inference endpoints that integrate into existing applications, data pipelines, or backend systems.

The platform fits into the Artificial Intelligence (AI) infrastructure and Machine Learning Operations (MLOps) categories, where it abstracts infrastructure elements such as containerization, orchestration, and cluster management, while still exposing configuration for resource allocation and scaling. Baseten supports deployment patterns that align with cloud-native practices, such as container-based runtimes and serverless-style autoscaling, including horizontal scaling based on request volume or latency targets. This allows teams to align ML inference with broader microservices and Application Programming Interface (API) strategies already in place within enterprise architectures.

From an integration perspective, Baseten centers on API-driven access to models. Deployed models are made available through Hypertext Transfer Protocol (HTTP) endpoints that can be called from web services, internal tools, or external products, enabling use cases such as personalization, recommendation, content generation, or classification. The platform provides model versioning and configuration options so that teams can control rollout strategies, maintain multiple versions of a model in parallel, and manage updates with predictable operational behavior.

Baseten’s feature set addresses observability and performance management for inference workloads. Logging, basic monitoring, and metrics help engineering and data science teams track request rates, response times, and resource utilization, and make configuration changes such as adjusting instance sizes or concurrency. Autoscaling capabilities and hardware selection, including Central Processing Unit (CPU) and Graphics Processing Unit (GPU) options when supported, help match infrastructure cost and performance to workload requirements, which is relevant for latency-sensitive or high-throughput scenarios.

In an enterprise context, Baseten functions as a specialized platform within categories such as AI infrastructure, MLOps, and developer platforms. It is positioned for teams that want to keep control of their models and application logic while offloading much of the operational overhead of serving, scaling, and maintaining inference endpoints. This makes it applicable for software engineering, data science, and platform engineering groups that collaborate on bringing ML features into production applications without building a full in-house serving and orchestration stack.